Project 1
Bailey Spiegelberg
2024-09-09
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#Load packages
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("here")## here() starts at C:/Users/bailey.spiegelberg/Desktop/biostat776project1#tests if a directory named "data" exists locally
if (!dir.exists(here("data"))) {
dir.create(here("data"))
}
# saves data only once (not each time you knit a R Markdown)
if (!file.exists(here("data", "chocolate.RDS"))) {
url_csv <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv"
chocolate <- readr::read_csv(url_csv)
# save the file to RDS objects
saveRDS(chocolate, file = here("data", "chocolate.RDS"))
}chocolate <- readRDS(here("data", "chocolate.RDS"))
as_tibble(chocolate)## # A tibble: 2,530 × 10
## ref company_manufacturer company_location review_date
## <dbl> <chr> <chr> <dbl>
## 1 2454 5150 U.S.A. 2019
## 2 2458 5150 U.S.A. 2019
## 3 2454 5150 U.S.A. 2019
## 4 2542 5150 U.S.A. 2021
## 5 2546 5150 U.S.A. 2021
## 6 2546 5150 U.S.A. 2021
## 7 2542 5150 U.S.A. 2021
## 8 797 A. Morin France 2012
## 9 797 A. Morin France 2012
## 10 1011 A. Morin France 2013
## # ℹ 2,520 more rows
## # ℹ 6 more variables: country_of_bean_origin <chr>,
## # specific_bean_origin_or_bar_name <chr>, cocoa_percent <chr>,
## # ingredients <chr>, most_memorable_characteristics <chr>, rating <dbl>glimpse(chocolate)## Rows: 2,530
## Columns: 10
## $ ref <dbl> 2454, 2458, 2454, 2542, 2546, 2546, 2…
## $ company_manufacturer <chr> "5150", "5150", "5150", "5150", "5150…
## $ company_location <chr> "U.S.A.", "U.S.A.", "U.S.A.", "U.S.A.…
## $ review_date <dbl> 2019, 2019, 2019, 2021, 2021, 2021, 2…
## $ country_of_bean_origin <chr> "Tanzania", "Dominican Republic", "Ma…
## $ specific_bean_origin_or_bar_name <chr> "Kokoa Kamili, batch 1", "Zorzal, bat…
## $ cocoa_percent <chr> "76%", "76%", "76%", "68%", "72%", "8…
## $ ingredients <chr> "3- B,S,C", "3- B,S,C", "3- B,S,C", "…
## $ most_memorable_characteristics <chr> "rich cocoa, fatty, bready", "cocoa, …
## $ rating <dbl> 3.25, 3.50, 3.75, 3.00, 3.00, 3.25, 3…Part1: Explore data
library(dplyr)
library(ggplot2)- Make a histogram of the rating scores to visualize the overall distribution of scores. Change the number of bins from the default to 10, 15, 20, and 25. Pick on the one that you think looks the best. Explain what the difference is when you change the number of bins and explain why you picked the one you did.
Changing the the number of bins affects how many bars in the histogram will be present. For example, with the default (30 bins) there are 12 bars but with (bins = 10), there are 9 bars. I chose (bins = 15) as I think it gives us the best distribution.
ggplot(chocolate, aes(x=rating)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(chocolate, aes(x=rating)) + geom_histogram(bins = 10)
ggplot(chocolate, aes(x=rating)) + geom_histogram(bins = 15)
ggplot(chocolate, aes(x=rating)) + geom_histogram(bins = 20)
ggplot(chocolate, aes(x=rating)) + geom_histogram(bins = 25)
library(ThemePark)
ggplot(chocolate, aes(x=rating)) +
geom_histogram (bins = 15) +
labs(title = "Rating Distribution", x = "Rating", y = "Count") +
theme_dune()
- Consider the countries where the beans originated from. How many reviews come from each country of bean origin?
countrybeanreview = chocolate %>%
group_by(country_of_bean_origin) %>%
summarise (n())
print(countrybeanreview)## # A tibble: 62 × 2
## country_of_bean_origin `n()`
## <chr> <int>
## 1 Australia 3
## 2 Belize 76
## 3 Blend 156
## 4 Bolivia 80
## 5 Brazil 78
## 6 Burma 1
## 7 Cameroon 3
## 8 China 1
## 9 Colombia 79
## 10 Congo 11
## # ℹ 52 more rows- What is average rating scores from reviews of chocolate bars that have Ecuador as country_of_bean_origin in this dataset? For this same set of reviews, also calculate (1) the total number of reviews and (2) the standard deviation of the rating scores. Your answer should be a new data frame with these three summary statistics in three columns. Label the name of these columns mean, sd, and total.
Ecuador = chocolate %>%
group_by(country_of_bean_origin =="Ecuador") %>%
summarise (mean = mean(rating), total = n(), sd = sd(rating))
print (Ecuador)## # A tibble: 2 × 4
## `country_of_bean_origin == "Ecuador"` mean total sd
## <lgl> <dbl> <int> <dbl>
## 1 FALSE 3.20 2311 0.438
## 2 TRUE 3.16 219 0.512- Which company (name) makes the best chocolate (or has the highest ratings on average) with beans from Ecuador?
The companies with the highest ratings are Amano, Benoit Nihant, Beschle, Domori, Durci, Pacari, and The Smooth Chocolator
bestcompany = chocolate %>%
filter(country_of_bean_origin =="Ecuador" & rating == 4) %>%
group_by(company_manufacturer)
print (bestcompany)## # A tibble: 8 × 10
## # Groups: company_manufacturer [7]
## ref company_manufacturer company_location review_date country_of_bean_origin
## <dbl> <chr> <chr> <dbl> <chr>
## 1 470 Amano U.S.A. 2010 Ecuador
## 2 1141 Benoit Nihant Belgium 2013 Ecuador
## 3 636 Beschle (Felchlin) Switzerland 2011 Ecuador
## 4 192 Domori Italy 2007 Ecuador
## 5 1630 Durci U.S.A. 2015 Ecuador
## 6 1415 Pacari Ecuador 2014 Ecuador
## 7 1622 Smooth Chocolator, … Australia 2015 Ecuador
## 8 1740 Smooth Chocolator, … Australia 2016 Ecuador
## # ℹ 5 more variables: specific_bean_origin_or_bar_name <chr>,
## # cocoa_percent <chr>, ingredients <chr>,
## # most_memorable_characteristics <chr>, rating <dbl>- Calculate the average rating across all country of origins for beans. Which top 3 countries (for bean origin) have the highest ratings on average?
Top countries with highest average ratings are Tobago, China and Sao Tome & Principe.
countryaverage = chocolate %>%
group_by(country_of_bean_origin) %>%
summarise (mean = mean(rating)) %>%
arrange(desc(mean))
print (countryaverage)## # A tibble: 62 × 2
## country_of_bean_origin mean
## <chr> <dbl>
## 1 Tobago 3.62
## 2 China 3.5
## 3 Sao Tome & Principe 3.5
## 4 Solomon Islands 3.45
## 5 Congo 3.32
## 6 Thailand 3.3
## 7 Cuba 3.29
## 8 Vietnam 3.29
## 9 Papua New Guinea 3.28
## 10 Madagascar 3.27
## # ℹ 52 more rows- Following up on the previous problem, now remove any countries of bean origins that have less than 10 chocolate bar reviews. Now, which top 3 countries have the highest ratings on average?
Now top countries with highest average ratings are Solomon Islands, Congo and Cuba.
morethan10 = chocolate %>%
group_by(country_of_bean_origin) %>%
filter(n()>=10) %>%
summarise (mean = mean(rating)) %>%
arrange(desc(mean))
print (morethan10)## # A tibble: 35 × 2
## country_of_bean_origin mean
## <chr> <dbl>
## 1 Solomon Islands 3.45
## 2 Congo 3.32
## 3 Cuba 3.29
## 4 Vietnam 3.29
## 5 Papua New Guinea 3.28
## 6 Madagascar 3.27
## 7 Haiti 3.27
## 8 Brazil 3.26
## 9 Guatemala 3.26
## 10 Nicaragua 3.26
## # ℹ 25 more rows- For this last part, let’s explore the relationship between percent chocolate and ratings.
library(lubridate)
library(tidyr)#7.1 Identify the countries of bean origin with at least 50 reviews. Remove reviews from countries are not in this list.
atleast50 = chocolate %>%
group_by(country_of_bean_origin) %>%
summarise(reviews = n()) %>%
filter(reviews >=50)
print (atleast50)## # A tibble: 16 × 2
## country_of_bean_origin reviews
## <chr> <int>
## 1 Belize 76
## 2 Blend 156
## 3 Bolivia 80
## 4 Brazil 78
## 5 Colombia 79
## 6 Dominican Republic 226
## 7 Ecuador 219
## 8 Guatemala 62
## 9 Madagascar 177
## 10 Mexico 55
## 11 Nicaragua 100
## 12 Papua New Guinea 50
## 13 Peru 244
## 14 Tanzania 79
## 15 Venezuela 253
## 16 Vietnam 73#7.2 Using the variable describing the chocolate percentage for each review, create a new column that groups chocolate percentages into one of four groups: (i) <60%, (ii) >=60 to <70%, (iii) >=70 to <90%, and (iii) >=90%
atleast50 = chocolate %>%
group_by(country_of_bean_origin) %>%
mutate(cocoa_percent_group = case_when(
cocoa_percent < 60 ~ "i",
cocoa_percent >= 60 & cocoa_percent < 70 ~ "ii",
cocoa_percent >= 70 & cocoa_percent < 90 ~ "iii",
cocoa_percent >= 90 ~ "iv" )) %>%
arrange(cocoa_percent_group) %>%
summarise(reviews = n(), cocoa_percent_group = cocoa_percent_group ) %>%
filter(reviews >=50) ## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `summarise()` has grouped output by 'country_of_bean_origin'. You can override
## using the `.groups` argument.print(atleast50)## # A tibble: 2,007 × 3
## # Groups: country_of_bean_origin [16]
## country_of_bean_origin reviews cocoa_percent_group
## <chr> <int> <chr>
## 1 Belize 76 ii
## 2 Belize 76 ii
## 3 Belize 76 ii
## 4 Belize 76 ii
## 5 Belize 76 ii
## 6 Belize 76 iii
## 7 Belize 76 iii
## 8 Belize 76 iii
## 9 Belize 76 iii
## 10 Belize 76 iii
## # ℹ 1,997 more rows#3. Using the new column described in #2, re-order the factor levels (if needed) to be starting with the smallest percentage group and increasing to the largest percentage group
atleast50 = chocolate %>%
group_by(country_of_bean_origin) %>%
mutate(cocoa_percent_group = case_when(
cocoa_percent < 60 ~ "i",
cocoa_percent >= 60 & cocoa_percent < 70 ~ "ii",
cocoa_percent >= 70 & cocoa_percent < 90 ~ "iii",
cocoa_percent >= 90 ~ "iv" )) %>%
summarise(reviews = n(), cocoa_percent_group = cocoa_percent_group ) %>%
filter(reviews >=50) %>%
arrange(cocoa_percent_group)## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `summarise()` has grouped output by 'country_of_bean_origin'. You can override
## using the `.groups` argument.print(atleast50)## # A tibble: 2,007 × 3
## # Groups: country_of_bean_origin [16]
## country_of_bean_origin reviews cocoa_percent_group
## <chr> <int> <chr>
## 1 Blend 156 i
## 2 Blend 156 i
## 3 Blend 156 i
## 4 Blend 156 i
## 5 Blend 156 i
## 6 Blend 156 i
## 7 Blend 156 i
## 8 Blend 156 i
## 9 Blend 156 i
## 10 Blend 156 i
## # ℹ 1,997 more rows#7.4 For each country, make a set of four side-by-side boxplots plotting the groups on the x-axis and the ratings on the y-axis. These plots should be faceted by country.
g = chocolate %>%
group_by(country_of_bean_origin) %>%
mutate(cocoa_percent_group = case_when(
cocoa_percent < 60 ~ "i",
cocoa_percent >= 60 & cocoa_percent < 70 ~ "ii",
cocoa_percent >= 70 & cocoa_percent < 90 ~ "iii",
cocoa_percent >= 90 ~ "iv" )) %>%
reframe(reviews = n(), cocoa_percent_group = cocoa_percent_group, rating = rating ) %>%
filter(reviews >=50) %>%
ggplot(aes(x = cocoa_percent_group, y = rating)) +
geom_boxplot (color = "deeppink") +
facet_wrap(~ country_of_bean_origin) +
labs(title = "Rating Of Cocoa Percent by Country", x = "cocoa percent group", y = "Rating") +
theme_barbie(plot.title = element_text(size=20, hjust = .5))
print (g)
On average, category ii is barely higher than category iii for highest ratings. Countries are split between category ii and iii on being the highest rated.
#Part 2: Join two datasets together
library(gapminder)
head(gapminder)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.#2.1 Use this dataset it to create a new column called continent in our chocolate dataset1 that contains the continent name for each review where the country of bean origin is.
chocolate =readRDS(here("data", "chocolate.RDS"))
Chocolatewithcontinents = right_join(chocolate, gapminder,
by = c("country_of_bean_origin" = "country"),
relationship = "many-to-many")
print(Chocolatewithcontinents)## # A tibble: 25,944 × 15
## ref company_manufacturer company_location review_date
## <dbl> <chr> <chr> <dbl>
## 1 2454 5150 U.S.A. 2019
## 2 2454 5150 U.S.A. 2019
## 3 2454 5150 U.S.A. 2019
## 4 2454 5150 U.S.A. 2019
## 5 2454 5150 U.S.A. 2019
## 6 2454 5150 U.S.A. 2019
## 7 2454 5150 U.S.A. 2019
## 8 2454 5150 U.S.A. 2019
## 9 2454 5150 U.S.A. 2019
## 10 2454 5150 U.S.A. 2019
## # ℹ 25,934 more rows
## # ℹ 11 more variables: country_of_bean_origin <chr>,
## # specific_bean_origin_or_bar_name <chr>, cocoa_percent <chr>,
## # ingredients <chr>, most_memorable_characteristics <chr>, rating <dbl>,
## # continent <fct>, year <int>, lifeExp <dbl>, pop <int>, gdpPercap <dbl>#2.2 Only keep reviews that have reviews from countries of bean origin with at least 10 reviews.
morethan10 = Chocolatewithcontinents %>%
group_by(country_of_bean_origin) %>%
summarise (reviews = n()) %>%
filter(n()>=10) %>%
arrange(desc(n()))
print (morethan10)## # A tibble: 142 × 2
## country_of_bean_origin reviews
## <chr> <int>
## 1 Afghanistan 12
## 2 Albania 12
## 3 Algeria 12
## 4 Angola 12
## 5 Argentina 12
## 6 Australia 36
## 7 Austria 12
## 8 Bahrain 12
## 9 Bangladesh 12
## 10 Belgium 12
## # ℹ 132 more rows#2.3 Also, remove the country of bean origin named "Blend".
Chocolatewithcontinents = Chocolatewithcontinents %>%
filter(country_of_bean_origin!="Blend")
print(Chocolatewithcontinents)## # A tibble: 25,944 × 15
## ref company_manufacturer company_location review_date
## <dbl> <chr> <chr> <dbl>
## 1 2454 5150 U.S.A. 2019
## 2 2454 5150 U.S.A. 2019
## 3 2454 5150 U.S.A. 2019
## 4 2454 5150 U.S.A. 2019
## 5 2454 5150 U.S.A. 2019
## 6 2454 5150 U.S.A. 2019
## 7 2454 5150 U.S.A. 2019
## 8 2454 5150 U.S.A. 2019
## 9 2454 5150 U.S.A. 2019
## 10 2454 5150 U.S.A. 2019
## # ℹ 25,934 more rows
## # ℹ 11 more variables: country_of_bean_origin <chr>,
## # specific_bean_origin_or_bar_name <chr>, cocoa_percent <chr>,
## # ingredients <chr>, most_memorable_characteristics <chr>, rating <dbl>,
## # continent <fct>, year <int>, lifeExp <dbl>, pop <int>, gdpPercap <dbl>NAcontinents = Chocolatewithcontinents %>%
filter(continent = NA)
print (NAcontinents)## # A tibble: 0 × 15
## # ℹ 15 variables: ref <dbl>, company_manufacturer <chr>,
## # company_location <chr>, review_date <dbl>, country_of_bean_origin <chr>,
## # specific_bean_origin_or_bar_name <chr>, cocoa_percent <chr>,
## # ingredients <chr>, most_memorable_characteristics <chr>, rating <dbl>,
## # continent <fct>, year <int>, lifeExp <dbl>, pop <int>, gdpPercap <dbl>violin = ggplot(Chocolatewithcontinents, aes(x = continent, y = rating)) +
geom_violin() +
labs (x = "Continents", y = "Ratings")
print (violin)## Warning: Removed 1248 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
#Part 3 Convert wide data into long data
chocolate =readRDS(here("data", "chocolate.RDS"))#3.1 Create a new set of columns titled beans, sugar, cocoa_butter, vanilla, letchin, and salt that contain a 1 or 0 representing whether or not that review for the chocolate bar contained that ingredient (1) or not (0).
ingredients_chocolate = chocolate %>%
select(ingredients) %>%
mutate(
beans = as.integer(grepl("B", ingredients)),
sugar = as.integer(grepl("S", ingredients)),
cocoa_butter = as.integer(grepl ("C", ingredients)),
vanilla = as.integer(grepl("V", ingredients)),
letchin = as.integer(grepl("L", ingredients)),
salt = as.integer(grepl("SA", ingredients)),
)
print(ingredients_chocolate)## # A tibble: 2,530 × 7
## ingredients beans sugar cocoa_butter vanilla letchin salt
## <chr> <int> <int> <int> <int> <int> <int>
## 1 3- B,S,C 1 1 1 0 0 0
## 2 3- B,S,C 1 1 1 0 0 0
## 3 3- B,S,C 1 1 1 0 0 0
## 4 3- B,S,C 1 1 1 0 0 0
## 5 3- B,S,C 1 1 1 0 0 0
## 6 3- B,S,C 1 1 1 0 0 0
## 7 3- B,S,C 1 1 1 0 0 0
## 8 4- B,S,C,L 1 1 1 0 1 0
## 9 4- B,S,C,L 1 1 1 0 1 0
## 10 4- B,S,C,L 1 1 1 0 1 0
## # ℹ 2,520 more rows#3.2 Create a new set of columns titled char_cocoa, char_sweet, char_nutty, char_creamy, char_roasty, char_earthy that contain a 1 or 0 representing whether or not that the most memorable characteristic for the chocolate bar had that word (1) or not (0).
new_chocolate = chocolate %>%
select(ingredients, most_memorable_characteristics) %>%
mutate(
char_cocoa = as.integer(grepl("cocoa", most_memorable_characteristics)),
char_sweet = as.integer(grepl("sweet", most_memorable_characteristics)),
char_nutty = as.integer(grepl ("nutty", most_memorable_characteristics)),
char_creamy = as.integer(grepl("creamy", most_memorable_characteristics)),
char_roasty = as.integer(grepl("roasty", most_memorable_characteristics)),
char_earthy = as.integer(grepl("earthy", most_memorable_characteristics)),
)
print(new_chocolate)## # A tibble: 2,530 × 8
## ingredients most_memorable_characteristics char_cocoa char_sweet char_nutty
## <chr> <chr> <int> <int> <int>
## 1 3- B,S,C rich cocoa, fatty, bready 1 0 0
## 2 3- B,S,C cocoa, vegetal, savory 1 0 0
## 3 3- B,S,C cocoa, blackberry, full body 1 0 0
## 4 3- B,S,C chewy, off, rubbery 0 0 0
## 5 3- B,S,C fatty, earthy, moss, nutty,chal… 0 0 1
## 6 3- B,S,C mildly bitter, basic cocoa, fat… 1 0 0
## 7 3- B,S,C milk brownie, macadamia,chewy 0 0 0
## 8 4- B,S,C,L vegetal, nutty 0 0 1
## 9 4- B,S,C,L fruity, melon, roasty 0 0 0
## 10 4- B,S,C,L brief fruit note, earthy, nutty 0 0 1
## # ℹ 2,520 more rows
## # ℹ 3 more variables: char_creamy <int>, char_roasty <int>, char_earthy <int>all_chocolate = chocolate %>%
select(ingredients, most_memorable_characteristics, review_date) %>%
mutate(
beans = as.integer(grepl("B", ingredients)),
sugar = as.integer(grepl("S", ingredients)),
cocoa_butter = as.integer(grepl ("C", ingredients)),
vanilla = as.integer(grepl("V", ingredients)),
letchin = as.integer(grepl("L", ingredients)),
salt = as.integer(grepl("SA", ingredients)),
char_cocoa = as.integer(grepl("cocoa", most_memorable_characteristics)),
char_sweet = as.integer(grepl("sweet", most_memorable_characteristics)),
char_nutty = as.integer(grepl ("nutty", most_memorable_characteristics)),
char_creamy = as.integer(grepl("creamy", most_memorable_characteristics)),
char_roasty = as.integer(grepl("roasty", most_memorable_characteristics)),
char_earthy = as.integer(grepl("earthy", most_memorable_characteristics)),
)
print (all_chocolate)## # A tibble: 2,530 × 15
## ingredients most_memorable_characteris…¹ review_date beans sugar cocoa_butter
## <chr> <chr> <dbl> <int> <int> <int>
## 1 3- B,S,C rich cocoa, fatty, bready 2019 1 1 1
## 2 3- B,S,C cocoa, vegetal, savory 2019 1 1 1
## 3 3- B,S,C cocoa, blackberry, full body 2019 1 1 1
## 4 3- B,S,C chewy, off, rubbery 2021 1 1 1
## 5 3- B,S,C fatty, earthy, moss, nutty,… 2021 1 1 1
## 6 3- B,S,C mildly bitter, basic cocoa,… 2021 1 1 1
## 7 3- B,S,C milk brownie, macadamia,che… 2021 1 1 1
## 8 4- B,S,C,L vegetal, nutty 2012 1 1 1
## 9 4- B,S,C,L fruity, melon, roasty 2012 1 1 1
## 10 4- B,S,C,L brief fruit note, earthy, n… 2013 1 1 1
## # ℹ 2,520 more rows
## # ℹ abbreviated name: ¹​most_memorable_characteristics
## # ℹ 9 more variables: vanilla <int>, letchin <int>, salt <int>,
## # char_cocoa <int>, char_sweet <int>, char_nutty <int>, char_creamy <int>,
## # char_roasty <int>, char_earthy <int>#3.3 For each year (i.e. review_date), calculate the mean value in each new column you created across all reviews for that year.
mean_value = all_chocolate %>%
group_by(review_date) %>%
summarise(
mean_beans = mean(beans, na.rm = TRUE),
mean_sugar = mean(sugar, na.rm = TRUE),
mean_cocoa_butter = mean(cocoa_butter, na.rm = TRUE),
mean_vanilla = mean(vanilla, na.rm = TRUE),
mean_letchin = mean(letchin, na.rm = TRUE),
mean_salt = mean(salt, na.rm = TRUE),
mean_char_cocoa = mean(char_cocoa, na.rm = TRUE),
mean_char_sweet = mean(char_sweet, na.rm = TRUE),
mean_char_nutty = mean(char_nutty, na.rm = TRUE),
mean_char_creamy = mean(char_creamy, na.rm = TRUE),
mean_char_roasty = mean(char_roasty, na.rm = TRUE),
mean_char_earthy = mean(char_earthy, na.rm = TRUE),
)
str(mean_value)## tibble [16 × 13] (S3: tbl_df/tbl/data.frame)
## $ review_date : num [1:16] 2006 2007 2008 2009 2010 ...
## $ mean_beans : num [1:16] 0.968 0.945 0.913 0.919 0.855 ...
## $ mean_sugar : num [1:16] 0.968 0.945 0.902 0.919 0.855 ...
## $ mean_cocoa_butter: num [1:16] 0.903 0.767 0.75 0.772 0.709 ...
## $ mean_vanilla : num [1:16] 0.694 0.548 0.359 0.325 0.227 ...
## $ mean_letchin : num [1:16] 0.694 0.384 0.511 0.341 0.391 ...
## $ mean_salt : num [1:16] 0 0 0 0 0 0 0 0 0 0 ...
## $ mean_char_cocoa : num [1:16] 0.21 0.342 0.109 0.146 0.218 ...
## $ mean_char_sweet : num [1:16] 0.1613 0.0959 0.1304 0.1545 0.1 ...
## $ mean_char_nutty : num [1:16] 0.0323 0.0411 0.1522 0.1545 0.1455 ...
## $ mean_char_creamy : num [1:16] 0.2419 0.2329 0.0978 0.0894 0.0909 ...
## $ mean_char_roasty : num [1:16] 0.0484 0.0137 0.0435 0.0813 0.0364 ...
## $ mean_char_earthy : num [1:16] 0.0645 0.0685 0.0435 0.0732 0.0727 ...#3.4 Convert this wide dataset into a long dataset with a new feature and mean_score column.
long_mean = pivot_longer(
mean_value,
cols = starts_with("mean_"),
names_to = "features",
values_to = "mean_score"
)
print(long_mean)## # A tibble: 192 × 3
## review_date features mean_score
## <dbl> <chr> <dbl>
## 1 2006 mean_beans 0.968
## 2 2006 mean_sugar 0.968
## 3 2006 mean_cocoa_butter 0.903
## 4 2006 mean_vanilla 0.694
## 5 2006 mean_letchin 0.694
## 6 2006 mean_salt 0
## 7 2006 mean_char_cocoa 0.210
## 8 2006 mean_char_sweet 0.161
## 9 2006 mean_char_nutty 0.0323
## 10 2006 mean_char_creamy 0.242
## # ℹ 182 more rows#Part 4: Data visualization
scatter_plot = long_mean %>%
ggplot(aes(x = review_date, y = mean_score)) +
geom_point (color = "white") +
geom_smooth(color = "gold") +
facet_wrap(~features) +
labs(title = "Scatter plot of mean scores over time, subsetted by ingredients and characteristics", subtitle = "percentage of letchin and vanilla in chocolates have decreased over time, while mention of a cocoa charactistic has increased", caption = "Bailey Spiegelberg", x = "Time (review date)", y = "mean score") +
theme_alien(plot.title = element_text(size=10),
plot.subtitle = element_text(size=6),
plot.caption = element_text(size=8))
print (scatter_plot)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#Part 5: Make the worst plot you can!
- I named the x axis poorly, but in the future a good axis label will be important.
- I used geom_point, which is not a good way to represent this data, but in the future geom_point could be the appropriate way to represent data.
- I changed the colour of the dots to yellow, which makes it hard to see. But knowing how to change the colour is useful.
- I changed the size, shape, and linewidth of my dots, making it really hard to see. But knowing how to do this will help me make better graphs in the future/.
- I changed the theme, which could be captivating in the future.
- I changed the x axis font size, so that it is too big and you can’t read what it says. Knowing how to change axis font size is important.
- I don’t have a title, which is always important. But I know how to add one as seen in part 6.
badplot = chocolate %>%
ggplot(aes(
x = as.factor(review_date),
y = rating
)) +
geom_point(color = "yellow",
size = .5,
shape = 11,
fill = "red",
linewidth = 10) +
theme_asteroid_city(axis.text.x = element_text(size =15))## Warning in geom_point(color = "yellow", size = 0.5, shape = 11, fill = "red", :
## Ignoring unknown parameters: `linewidth`print(badplot)
#Part 6: Make my plot a better plot!
- I changed the x and y labels.
- I added a title.
- I changed the theme.
- I changed the size of x axis font in theme.
- I changed the size of font for the title.
- I made the graph interactive.
- I centered the title of the graph.
chocolate =readRDS(here("data", "chocolate.RDS"))
library("plotly")##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot = chocolate %>%
ggplot(aes(
x = as.factor(review_date),
y = rating,
fill = review_date
)) +
geom_violin(color = "green") +
labs(title = "Ratings over the Years", x = "Review Date", y = "Ratings", fill = "Review Date") +
theme_alien(plot.title = element_text(size=16, hjust = .5),
plot.caption = element_text(size=8),
axis.text.x = element_text(size =7))
plotly::ggplotly((plot))